Feature selective validation (FSV) for validation of computational electromagnetics (CEM). part I-the FSV method
TL;DR: A detailed analysis of the FSV method is presented, setting it firmly in the context of previous comparison techniques; it suggests the relationship between validation of graphically presented data and the psychology of visual perception, and a set of applicability tests to judge the effectiveness of computer-based CEM validation techniques.
Abstract: A goal for the validation of computational electromagnetics (CEM) is to provide the community with a simple computational method that can be used to predict the assessment of electromagnetic compatibility (EMC) data as it would be undertaken by individuals or teams of engineers. The benefits of being able to do this include quantifying the comparison of data that has hitherto only been assessed qualitatively, to provide the ability to track differences between model iterations, and to provide a means of capturing the variability and range of opinions of groups and teams of workers. The feature selective validation (FSV) technique shows great promise for achieving this goal. This paper presents a detailed analysis of the FSV method, setting it firmly in the context of previous comparison techniques; it suggests the relationship between validation of graphically presented data and the psychology of visual perception. A set of applicability tests to judge the effectiveness of computer-based CEM validation techniques is also proposed. This paper is followed by a detailed comparison with visual assessment, which is presented in Part II
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TL;DR: This paper addresses two specific issues related to the implementation of the FSV method, namely "how well does it produce results that agree with visual assessment?" and "what benefit can it provide in a practical validation environment?"
Abstract: The feature selective validation (FSV) method has been proposed as a technique to allow the objective, quantified, comparison of data for inter alia validation of computational electromagnetics. In the companion paper "Feature selective validation for validation of computational electromagnetics. Part I-The FSV method," the method was outlined in some detail. This paper addresses two specific issues related to the implementation of the FSV method, namely "how well does it produce results that agree with visual assessment?" and "what benefit can it provide in a practical validation environment?" The first of these questions is addressed by comparing the FSV output to the results of an extensive survey of EMC engineers from several countries. The second is approached via a case study analysis
357 citations
Cites background or methods from "Feature selective validation (FSV) ..."
...Part I [2] discussed correlation and reliability functions....
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...THE feature selective validation (FSV) method, which was first investigated by Martin [1], has been presented in [2]....
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TL;DR: In this paper, an analytical model for vias and traces is presented for simulation of multilayer interconnects at the package and printed circuit board levels, which can be applied to efficiently simulate a wide range of structures.
Abstract: Analytical models for vias and traces are presented for simulation of multilayer interconnects at the package and printed circuit board levels. Vias are modeled using an analytical formulation for the parallel-plate impedance and capacitive elements, whereas the trace-via transitions are described by modal decomposition. It is shown that the models can be applied to efficiently simulate a wide range of structures. Different scenarios are analyzed including thru-hole and buried vias, power vias, and coupled traces routed into different layers. By virtue of the modal decomposition, the proposed method is general enough to handle structures with mixed reference planes. For the first time, these models have been validated against full-wave methods and measurements up to 40 GHz. An improvement on the computation speed of at least two orders of magnitude has been observed with respect to full-wave simulations.
153 citations
TL;DR: In this article, a broadband bandpass frequency-selective surface (FSS) designed for 5G EMI shielding is proposed, which employs the vertical vias into the 2-D periodic arrays, and such a single 2.5D periodic layer of via-based structure is demonstrated to produce a highly stable angular response up to 60° for both TE and TM polarizations.
Abstract: A novel broadband bandpass frequency-selective surface (FSS) designed for fifth generation (5G) EMI shielding is proposed in this paper. This new design employs the vertical vias into the 2-D periodic arrays, and such a single 2.5-D periodic layer of via-based structure is demonstrated to produce a highly stable angular response up to 60° for both TE and TM polarizations. By cascading two layers of such 2.5-D periodic arrays, the proposed FSS is able to obtain a broad passband as well as the wide out-of-band rejection. Moreover, it has a quite sharp band edge between the passband and the specified stopband. A corresponding equivalent circuit model (ECM) is further developed for better analysis of the operating principle. Finally, a prototype working at the center frequency of around 28 GHz is fabricated and measured. The main novelty of this paper is introducing the 2.5-D concept into designing a wideband FSS, and further reduce the unit size as well as improve the angular stability. Favorable agreement is achieved among the 3-D full-wave simulation, ECM and measurement. All these results demonstrate that the proposed FSS is a good candidate for 5G EMI shielding.
103 citations
Cites background from "Feature selective validation (FSV) ..."
...nique [29], [30], if a quantitative assessment is preferred....
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TL;DR: In this article, the polynomial chaos method (PCM) and the method of moments (MoM) were used to quantify the uncertainty in CEM simulations, and the results showed that the PCM and the MoM are computationally more efficient than the MCM, but can provide poorer estimates of the uncertainty of the resonance compatibility data.
Abstract: Providing estimates of the uncertainty in results obtained by Computational Electromagnetic (CEM) simulations is essential when determining the acceptability of the results. The Monte Carlo method (MCM) has been previously used to quantify the uncertainty in CEM simulations. Other computationally efficient methods have been investigated more recently, such as the polynomial chaos method (PCM) and the method of moments (MoM). This paper introduces a novel implementation of the PCM and the MoM into the finite-difference time -domain method. The PCM and the MoM are found to be computationally more efficient than the MCM, but can provide poorer estimates of the uncertainty in resonant electromagnetic compatibility data.
96 citations
Cites methods from "Feature selective validation (FSV) ..."
...The feature selective validation (FSV) method [ 7 ], [8] is used in this paper to determine the similarity of the uncertainty curves formed from the different UA methods....
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TL;DR: In this article, a band-stop frequency selective surface (FSS) is proposed to provide effective shielding in X-band, with attenuation of at least 56 dB, and the proposed FSS provides 3-dB fractional bandwidth of 48% which is necessary to cover Xband.
Abstract: In this paper, a novel and miniaturized band-stop frequency selective surface (FSS) is presented. This FSS provides effective shielding in X-band, with attenuation of at least 56 dB. The proposed FSS provides 3-dB fractional bandwidth of 48% which is necessary to cover X-band. Moreover, the proposed design is polarization independent as it provides a stable frequency response at normal and oblique angles of incidences for both perpendicular TE and parallel TM wave modes. The copolarized and cross-polarized scattering parameter $S_{21}$ is analyzed at the selected band-stop/notch frequencies. More importantly, the proposed FSS is suitable for conformal applications and hence finds wider employability. A prototype of the proposed FSS is fabricated and tested. The measured results are in good agreement with the simulated results.
93 citations
Cites background from "Feature selective validation (FSV) ..."
...pare different data sets is reported in [35] and [36]....
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References
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Book•
01 Jan 1935
TL;DR: Routledge is now reissuing this prestigious series of 204 volumes originally published between 1910 and 1965, including works by key figures such as C.G. Jung, Sigmund Freud, Jean Piaget, Otto Rank, James Hillman, Erich Fromm, Karen Horney and Susan Isaacs as discussed by the authors.
Abstract: Routledge is now re-issuing this prestigious series of 204 volumes originally published between 1910 and 1965. The titles include works by key figures such asC.G. Jung, Sigmund Freud, Jean Piaget, Otto Rank, James Hillman, Erich Fromm, Karen Horney and Susan Isaacs. Each volume is available on its own, as part of a themed mini-set, or as part of a specially-priced 204-volume set. A brochure listing each title in the "International Library of Psychology" series is available upon request.
4,169 citations
TL;DR: Routledge is now reissuing this prestigious series of 204 volumes originally published between 1910 and 1965, including works by key figures such as C.G. Jung, Sigmund Freud, Jean Piaget, Otto Rank, James Hillman, Erich Fromm, Karen Horney and Susan Isaacs as mentioned in this paper.
Abstract: Routledge is now re-issuing this prestigious series of 204 volumes originally published between 1910 and 1965. The titles include works by key figures such asC.G. Jung, Sigmund Freud, Jean Piaget, Otto Rank, James Hillman, Erich Fromm, Karen Horney and Susan Isaacs. Each volume is available on its own, as part of a themed mini-set, or as part of a specially-priced 204-volume set. A brochure listing each title in the "International Library of Psychology" series is available upon request.
2,222 citations
TL;DR: In this article, a reduced r -factor r r = r r random where r random is the average value of r for randomly chosen pair of curves is calculated for more than 100 beams from 7 different surface structures and a direct relationship is established between the language of visual evaluation and numerical values of r ϵ.
300 citations
TL;DR: In this paper, the authors define five reliability factors (R-factors) which evaluate the reliability of a given surface structure analysis by calculating numerical values based on the degree of agreement between calculated and measured low-energy electron diffraction (LEED) intensity-voltage (I-V ) curves.
127 citations
"Feature selective validation (FSV) ..." refers background in this paper
...[11] M. A. van Hove, S. Y. Tong, and M. H. Elconin, “Surface structure refinements of 2H-MoS2, 2H-NbSe2 and W(100)p(2x1)-...
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...One of the more interesting additions has been that of van Hove [11]....
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...One of the most significant steps involved with the van Hove R-factor, and used specifically in the FSV method, is the decomposition of the original data into components with each of the components compared individually....
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Dissertation•
01 Jan 1999
TL;DR: This thesis details the development of the Feature Selective Validation (FSV) method, which builds on the common language of engineers and scientists alike, employing categories which relate to human interpretations of comparisons, namely: 'ideal', 'excellent', 'very good', 'good', 'fair', 'poor' and 'extremely poor' .
Abstract: Historically, validation has been perfonned on a case study basis employing visual evaluations, gradually inspiring confidence through continual application. At present, the method of visual evaluation is the most prevalent form of data analysis, as the brain is the best pattern recognition device known. However, the human visual/perceptual system is a complicated mechanism, prone to many types of physical and psychological influences. Fatigue is a major source of inaccuracy within the results of subjects perfonning complex visual evaluation tasks. Whilst physical and experiential differences along with age have an enormous bearing on the visual evaluation results of different subjects. It is to this end that automated methods of validation must be developed to produce repeatable, quantitative and objective verification results. This thesis details the development of the Feature Selective Validation (FSV) method. The FSV method comprises two component measures based on amplitude differences and feature differences. These measures are combined employing a measured level of subjectivity to fonn an overall assessment of the comparison in question or global difference. The three measures within the FSV method are strengthened by statistical analysis in the form of confidence levels based on amplitude, feature or global discrepancies between compared signals. Highly detailed diagnostic infonnation on the location and magnitude of discrepancies is also made available through the employment of graphical (discrete) representations of the three measures. The FSV method also benefits from the ability to mirror human perception, whilst producing infonnation which directly relates human variability and the confidence associated with it. The FSV method builds on the common language of engineers and scientists alike, employing categories which relate to human interpretations of comparisons, namely: 'ideal', 'excellent', 'very good', 'good', 'fair', 'poor' and 'extremely poor' . Quantitative Data Validation Automated Visual Evaluations II Anthony John Michael Martin PhD Thesis ACKNOWLEDGEMENTS My thanks go to my supervisor and mentor Dr. Alistair Duffy for his enduring opinions, encouragement, guidance and continual support. I gratefully acknowledge the invaluable help of the following people in the preparation of this thesis: Dr. Celine Turner, Paul Cartright, Trevor Benson and Malcolm Woolfson. Special thanks go to my parents and my fiancee for their support. Without their patience this thesis would not have been possible. Quantitative Data Validation Automated Visual Evaluations III Anthony John Michael Martin PhD Thesis CONTENTS
24 citations
"Feature selective validation (FSV) ..." refers methods in this paper
...This difference approach was used in the original formulation [2] and in the current free-standing application produced by the authors [12]....
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...The feature selective validation (FSV) technique has been developed specifically to achieve this aim [2]....
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